# PMPGuard: Catching Pseudo-Matched Pairs in Remote Sensing Image-Text Retrieval

arXiv: https://arxiv.org/abs/2512.18660 · HTML: https://mlsanity.com/paper/2512.18660/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 40 references that are in the map.
- First posted on arXiv 21 Dec 2025.

## Builds on (most cited first, 15 of 15)

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- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1408.5882/) (2014)
- [Exploring Models and Data for Remote Sensing Image Caption Generation](https://mlsanity.com/paper/1712.07835/) (2017)
- [RS5M and GeoRSCLIP: A Large Scale Vision-Language Dataset and A Large Vision-Language Model for Remote Sensing](https://mlsanity.com/paper/2306.11300/) (2023)
- [Recurrent Feature Reasoning for Image Inpainting](https://mlsanity.com/paper/2008.03737/) (2020)
- [Supporting Clustering with Contrastive Learning](https://mlsanity.com/paper/2103.12953/) (2021)
- [Learning Dynamic Routing for Semantic Segmentation](https://mlsanity.com/paper/2003.10401/) (2020)
- [Dynamic Graph Message Passing Networks](https://mlsanity.com/paper/1908.06955/) (2019)
- [Decoupled Dynamic Filter Networks](https://mlsanity.com/paper/2104.14107/) (2021)
- [Locate Anything on Earth: Advancing Open-Vocabulary Object Detection for Remote Sensing Community](https://mlsanity.com/paper/2408.09110/) (2024)
- [BiCro: Noisy Correspondence Rectification for Multi-modality Data via Bi-directional Cross-modal Similarity Consistency](https://mlsanity.com/paper/2303.12419/) (2023)
- [Towards Robust Multimodal Emotion Recognition under Missing Modalities and Distribution Shifts](https://mlsanity.com/paper/2506.10452/) (2025)
- [Efficient Remote Sensing with Harmonized Transfer Learning and Modality Alignment](https://mlsanity.com/paper/2404.18253/) (2024)
- [Semantic-Aware Ship Detection with Vision-Language Integration](https://mlsanity.com/paper/2508.15930/) (2025)

## Abstract (arXiv)

Remote sensing (RS) image-text retrieval faces significant challenges in real-world datasets due to the presence of Pseudo-Matched Pairs (PMPs), semantically mismatched or weakly aligned image-text pairs, which hinder the learning of reliable cross-modal alignments. To address this issue, we propose a novel retrieval framework that leverages Cross-Modal Gated Attention and a Positive-Negative Awareness Attention mechanism to mitigate the impact of such noisy associations. The gated module dynamically regulates cross-modal information flow, while the awareness mechanism explicitly distinguishes informative (positive) cues from misleading (negative) ones during alignment learning. Extensive experiments on three benchmark RS datasets, i.e., RSICD, RSITMD, and RS5M, demonstrate that our method consistently achieves state-of-the-art performance, highlighting its robustness and effectiveness in handling real-world mismatches and PMPs in RS image-text retrieval tasks.